Overview

Dataset statistics

Number of variables13
Number of observations8369
Missing cells0
Missing cells (%)0.0%
Duplicate rows1
Duplicate rows (%)< 0.1%
Total size in memory850.1 KiB
Average record size in memory104.0 B

Variable types

Numeric8
Categorical2
Text3

Alerts

Dataset has 1 (< 0.1%) duplicate rowsDuplicates
rooms is highly overall correlated with garages and 2 other fieldsHigh correlation
garages is highly overall correlated with rooms and 2 other fieldsHigh correlation
useful_area is highly overall correlated with rooms and 2 other fieldsHigh correlation
value is highly overall correlated with rooms and 2 other fieldsHigh correlation

Reproduction

Analysis started2023-08-07 19:13:44.147981
Analysis finished2023-08-07 19:13:56.179302
Duration12.03 seconds
Software versionydata-profiling vv4.4.0
Download configurationconfig.json

Variables

rooms
Real number (ℝ)

HIGH CORRELATION 

Distinct8
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.8392878
Minimum1
Maximum8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size65.5 KiB
2023-08-07T16:13:56.384197image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q33
95-th percentile4
Maximum8
Range7
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.89592517
Coefficient of variation (CV)0.31554573
Kurtosis-0.33134813
Mean2.8392878
Median Absolute Deviation (MAD)1
Skewness-0.24796203
Sum23762
Variance0.8026819
MonotonicityNot monotonic
2023-08-07T16:13:56.527957image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
3 3579
42.8%
2 2112
25.2%
4 1983
23.7%
1 654
 
7.8%
5 34
 
0.4%
6 5
 
0.1%
7 1
 
< 0.1%
8 1
 
< 0.1%
ValueCountFrequency (%)
1 654
 
7.8%
2 2112
25.2%
3 3579
42.8%
4 1983
23.7%
5 34
 
0.4%
6 5
 
0.1%
7 1
 
< 0.1%
8 1
 
< 0.1%
ValueCountFrequency (%)
8 1
 
< 0.1%
7 1
 
< 0.1%
6 5
 
0.1%
5 34
 
0.4%
4 1983
23.7%
3 3579
42.8%
2 2112
25.2%
1 654
 
7.8%

garages
Real number (ℝ)

HIGH CORRELATION 

Distinct11
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.0330983
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size65.5 KiB
2023-08-07T16:13:56.681086image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q33
95-th percentile4
Maximum12
Range11
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.121227
Coefficient of variation (CV)0.55148687
Kurtosis1.8712973
Mean2.0330983
Median Absolute Deviation (MAD)1
Skewness1.1509314
Sum17015
Variance1.2571501
MonotonicityNot monotonic
2023-08-07T16:13:56.832412image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
1 3416
40.8%
2 2536
30.3%
3 1436
17.2%
4 771
 
9.2%
5 151
 
1.8%
6 47
 
0.6%
7 5
 
0.1%
8 4
 
< 0.1%
9 1
 
< 0.1%
12 1
 
< 0.1%
ValueCountFrequency (%)
1 3416
40.8%
2 2536
30.3%
3 1436
17.2%
4 771
 
9.2%
5 151
 
1.8%
6 47
 
0.6%
7 5
 
0.1%
8 4
 
< 0.1%
9 1
 
< 0.1%
10 1
 
< 0.1%
ValueCountFrequency (%)
12 1
 
< 0.1%
10 1
 
< 0.1%
9 1
 
< 0.1%
8 4
 
< 0.1%
7 5
 
0.1%
6 47
 
0.6%
5 151
 
1.8%
4 771
 
9.2%
3 1436
17.2%
2 2536
30.3%

useful_area
Real number (ℝ)

HIGH CORRELATION 

Distinct426
Distinct (%)5.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean145.13825
Minimum25
Maximum826
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size65.5 KiB
2023-08-07T16:13:56.997951image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum25
5-th percentile48
Q180
median120
Q3190
95-th percentile320
Maximum826
Range801
Interquartile range (IQR)110

Descriptive statistics

Standard deviation88.90856
Coefficient of variation (CV)0.61257842
Kurtosis2.9981681
Mean145.13825
Median Absolute Deviation (MAD)50
Skewness1.47175
Sum1214662
Variance7904.732
MonotonicityNot monotonic
2023-08-07T16:13:57.196606image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
70 160
 
1.9%
110 151
 
1.8%
120 144
 
1.7%
90 138
 
1.6%
100 136
 
1.6%
80 130
 
1.6%
140 117
 
1.4%
130 113
 
1.4%
75 108
 
1.3%
60 108
 
1.3%
Other values (416) 7064
84.4%
ValueCountFrequency (%)
25 1
 
< 0.1%
26 1
 
< 0.1%
27 2
 
< 0.1%
28 5
 
0.1%
29 3
 
< 0.1%
30 17
0.2%
31 6
 
0.1%
32 6
 
0.1%
33 12
0.1%
34 14
0.2%
ValueCountFrequency (%)
826 1
 
< 0.1%
700 2
< 0.1%
692 1
 
< 0.1%
675 1
 
< 0.1%
660 1
 
< 0.1%
650 1
 
< 0.1%
640 1
 
< 0.1%
600 3
< 0.1%
550 4
< 0.1%
549 1
 
< 0.1%

latitude
Real number (ℝ)

Distinct6266
Distinct (%)74.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-23.578133
Minimum-23.647398
Maximum-23.491429
Zeros0
Zeros (%)0.0%
Negative8369
Negative (%)100.0%
Memory size65.5 KiB
2023-08-07T16:13:57.404896image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-23.647398
5-th percentile-23.631059
Q1-23.606055
median-23.576006
Q3-23.552001
95-th percentile-23.529389
Maximum-23.491429
Range0.1559692
Interquartile range (IQR)0.0540533

Descriptive statistics

Standard deviation0.033658857
Coefficient of variation (CV)-0.0014275454
Kurtosis-0.78193656
Mean-23.578133
Median Absolute Deviation (MAD)0.0278532
Skewness0.064543009
Sum-197325.4
Variance0.0011329186
MonotonicityNot monotonic
2023-08-07T16:13:57.599730image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-23.601446 26
 
0.3%
-23.6366667 22
 
0.3%
-23.6011079 21
 
0.3%
-23.5652892 15
 
0.2%
-23.5713184 15
 
0.2%
-23.6347054 11
 
0.1%
-23.5097595 11
 
0.1%
-23.6390494 11
 
0.1%
-23.6330678 10
 
0.1%
-23.5486537 10
 
0.1%
Other values (6256) 8217
98.2%
ValueCountFrequency (%)
-23.6473981 1
< 0.1%
-23.6472832 1
< 0.1%
-23.6472723 2
< 0.1%
-23.6472437 1
< 0.1%
-23.6471752 2
< 0.1%
-23.6471218 1
< 0.1%
-23.6470599 2
< 0.1%
-23.6470516 1
< 0.1%
-23.6470464 1
< 0.1%
-23.6470444 1
< 0.1%
ValueCountFrequency (%)
-23.4914289 1
< 0.1%
-23.4915289 1
< 0.1%
-23.4915443 1
< 0.1%
-23.4915744 1
< 0.1%
-23.491579 1
< 0.1%
-23.4916163 1
< 0.1%
-23.4916249 1
< 0.1%
-23.4916299 1
< 0.1%
-23.4916584 1
< 0.1%
-23.4917384 1
< 0.1%

longitude
Real number (ℝ)

Distinct6272
Distinct (%)74.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-46.666587
Minimum-46.739607
Maximum-46.567446
Zeros0
Zeros (%)0.0%
Negative8369
Negative (%)100.0%
Memory size65.5 KiB
2023-08-07T16:13:57.793796image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-46.739607
5-th percentile-46.730893
Q1-46.685372
median-46.665436
Q3-46.645129
95-th percentile-46.607742
Maximum-46.567446
Range0.1721615
Interquartile range (IQR)0.040243

Descriptive statistics

Standard deviation0.034280021
Coefficient of variation (CV)-0.00073457312
Kurtosis0.14454882
Mean-46.666587
Median Absolute Deviation (MAD)0.020146
Skewness0.043907939
Sum-390552.67
Variance0.0011751198
MonotonicityNot monotonic
2023-08-07T16:13:57.983237image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-46.717651 26
 
0.3%
-46.73 22
 
0.3%
-46.7161175 21
 
0.3%
-46.6349529 15
 
0.2%
-46.6630318 15
 
0.2%
-46.7007211 11
 
0.1%
-46.7146293 11
 
0.1%
-46.6507406 11
 
0.1%
-46.5953517 10
 
0.1%
-46.7276921 10
 
0.1%
Other values (6262) 8217
98.2%
ValueCountFrequency (%)
-46.7396075 2
< 0.1%
-46.7395826 2
< 0.1%
-46.7395789 1
 
< 0.1%
-46.7395722 1
 
< 0.1%
-46.7395677 1
 
< 0.1%
-46.7395479 1
 
< 0.1%
-46.7395249 1
 
< 0.1%
-46.7394959 1
 
< 0.1%
-46.7394457 3
< 0.1%
-46.7394285 2
< 0.1%
ValueCountFrequency (%)
-46.567446 1
 
< 0.1%
-46.5675586 1
 
< 0.1%
-46.5675631 3
< 0.1%
-46.5676633 1
 
< 0.1%
-46.5677867 1
 
< 0.1%
-46.5678154 1
 
< 0.1%
-46.567842 2
< 0.1%
-46.5678545 2
< 0.1%
-46.5678852 1
 
< 0.1%
-46.5679589 1
 
< 0.1%

value
Real number (ℝ)

HIGH CORRELATION 

Distinct7844
Distinct (%)93.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1478880.6
Minimum175101
Maximum4999430
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size65.5 KiB
2023-08-07T16:13:58.176051image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum175101
5-th percentile424585
Q1731354
median1161620
Q31960360
95-th percentile3668298
Maximum4999430
Range4824329
Interquartile range (IQR)1229006

Descriptive statistics

Standard deviation1002856.1
Coefficient of variation (CV)0.67811839
Kurtosis1.202007
Mean1478880.6
Median Absolute Deviation (MAD)530438
Skewness1.2891742
Sum1.2376751 × 1010
Variance1.0057204 × 1012
MonotonicityNot monotonic
2023-08-07T16:13:58.375872image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
297708 7
 
0.1%
1123800 6
 
0.1%
840479 5
 
0.1%
499316 5
 
0.1%
1093710 5
 
0.1%
624407 5
 
0.1%
832444 5
 
0.1%
1288730 4
 
< 0.1%
609607 4
 
< 0.1%
837118 4
 
< 0.1%
Other values (7834) 8319
99.4%
ValueCountFrequency (%)
175101 1
 
< 0.1%
191894 1
 
< 0.1%
192101 1
 
< 0.1%
194056 1
 
< 0.1%
196269 1
 
< 0.1%
196852 1
 
< 0.1%
211209 1
 
< 0.1%
219560 1
 
< 0.1%
220212 1
 
< 0.1%
221027 3
< 0.1%
ValueCountFrequency (%)
4999430 1
< 0.1%
4999320 1
< 0.1%
4994770 1
< 0.1%
4990650 1
< 0.1%
4985270 1
< 0.1%
4983680 1
< 0.1%
4983190 1
< 0.1%
4977320 1
< 0.1%
4976850 1
< 0.1%
4965330 1
< 0.1%

interior_quality
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size65.5 KiB
1
2811 
2
2786 
3
2772 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters8369
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row3
3rd row2
4th row3
5th row2

Common Values

ValueCountFrequency (%)
1 2811
33.6%
2 2786
33.3%
3 2772
33.1%

Length

2023-08-07T16:13:58.553593image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-07T16:13:58.694930image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 2811
33.6%
2 2786
33.3%
3 2772
33.1%

Most occurring characters

ValueCountFrequency (%)
1 2811
33.6%
2 2786
33.3%
3 2772
33.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 8369
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 2811
33.6%
2 2786
33.3%
3 2772
33.1%

Most occurring scripts

ValueCountFrequency (%)
Common 8369
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 2811
33.6%
2 2786
33.3%
3 2772
33.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 8369
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 2811
33.6%
2 2786
33.3%
3 2772
33.1%

time_on_market
Real number (ℝ)

Distinct4788
Distinct (%)57.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean114.66114
Minimum0.097115531
Maximum359.82024
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size65.5 KiB
2023-08-07T16:13:58.849043image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.097115531
5-th percentile6
Q134
median88.030113
Q3177.02297
95-th percentile307.3791
Maximum359.82024
Range359.72313
Interquartile range (IQR)143.02297

Descriptive statistics

Standard deviation95.152735
Coefficient of variation (CV)0.82986035
Kurtosis-0.3857524
Mean114.66114
Median Absolute Deviation (MAD)62.595799
Skewness0.80676853
Sum959599.09
Variance9054.043
MonotonicityNot monotonic
2023-08-07T16:13:59.031004image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 64
 
0.8%
2 54
 
0.6%
11 50
 
0.6%
13 48
 
0.6%
28 47
 
0.6%
7 47
 
0.6%
23 46
 
0.5%
5 45
 
0.5%
6 44
 
0.5%
4 43
 
0.5%
Other values (4778) 7881
94.2%
ValueCountFrequency (%)
0.09711553064 1
< 0.1%
0.142504697 1
< 0.1%
0.1432957567 1
< 0.1%
0.1760284416 1
< 0.1%
0.2374070195 1
< 0.1%
0.2410805883 1
< 0.1%
0.2618874258 1
< 0.1%
0.3049262501 1
< 0.1%
0.3848981899 1
< 0.1%
0.4402768998 1
< 0.1%
ValueCountFrequency (%)
359.8202427 1
< 0.1%
359.8105072 1
< 0.1%
359.6643219 1
< 0.1%
359.6058738 1
< 0.1%
359.0785544 1
< 0.1%
358.8971827 1
< 0.1%
358.832001 1
< 0.1%
358.7832063 1
< 0.1%
358.5659347 1
< 0.1%
358.4602374 1
< 0.1%

sold
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size65.5 KiB
0
4484 
1
3885 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters8369
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 4484
53.6%
1 3885
46.4%

Length

2023-08-07T16:13:59.206231image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-07T16:13:59.335302image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 4484
53.6%
1 3885
46.4%

Most occurring characters

ValueCountFrequency (%)
0 4484
53.6%
1 3885
46.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 8369
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 4484
53.6%
1 3885
46.4%

Most occurring scripts

ValueCountFrequency (%)
Common 8369
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 4484
53.6%
1 3885
46.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 8369
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 4484
53.6%
1 3885
46.4%

Unnamed: 0
Real number (ℝ)

Distinct6292
Distinct (%)75.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2834.0705
Minimum0
Maximum6291
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size65.5 KiB
2023-08-07T16:13:59.486229image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile233
Q11226
median2677
Q34365
95-th percentile5880.6
Maximum6291
Range6291
Interquartile range (IQR)3139

Descriptive statistics

Standard deviation1817.2521
Coefficient of variation (CV)0.64121627
Kurtosis-1.1630305
Mean2834.0705
Median Absolute Deviation (MAD)1546
Skewness0.20549326
Sum23718336
Variance3302405.3
MonotonicityNot monotonic
2023-08-07T16:13:59.766943image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
179 26
 
0.3%
283 22
 
0.3%
298 21
 
0.3%
1940 15
 
0.2%
369 15
 
0.2%
513 11
 
0.1%
1193 11
 
0.1%
1172 11
 
0.1%
2643 10
 
0.1%
395 10
 
0.1%
Other values (6282) 8217
98.2%
ValueCountFrequency (%)
0 1
 
< 0.1%
1 1
 
< 0.1%
2 1
 
< 0.1%
3 1
 
< 0.1%
4 1
 
< 0.1%
5 2
< 0.1%
6 1
 
< 0.1%
7 1
 
< 0.1%
8 3
< 0.1%
9 1
 
< 0.1%
ValueCountFrequency (%)
6291 1
< 0.1%
6290 1
< 0.1%
6289 1
< 0.1%
6288 1
< 0.1%
6287 1
< 0.1%
6286 1
< 0.1%
6285 1
< 0.1%
6284 1
< 0.1%
6283 1
< 0.1%
6282 1
< 0.1%
Distinct6136
Distinct (%)73.3%
Missing0
Missing (%)0.0%
Memory size65.5 KiB
2023-08-07T16:14:00.061172image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length140
Median length120
Mean length77.175768
Min length55

Characters and Unicode

Total characters645884
Distinct characters95
Distinct categories12 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4689 ?
Unique (%)56.0%

Sample

1st rowR. José de Oliveira Coelho, 200 - Vila Andrade, São Paulo - SP, 05727-240, Brazil
2nd rowR. Araquã, 70 - Bela Vista, São Paulo - SP, 01306-020, Brazil
3rd rowRua Correia de Lemos, 327 - Vila da Saúde, São Paulo - SP, 04140-000, Brazil
4th rowR. Itapura, 129 - Vila Gomes Cardim, São Paulo - SP, 03310-000, Brazil
5th rowR. Garapeba, 255 - Jardim Vila Mariana, São Paulo - SP, 04116-210, Brazil
ValueCountFrequency (%)
18383
 
16.0%
são 8503
 
7.4%
paulo 8452
 
7.4%
sp 8369
 
7.3%
brazil 8369
 
7.3%
r 5568
 
4.8%
vila 2339
 
2.0%
de 1270
 
1.1%
edifício 1248
 
1.1%
rua 1203
 
1.0%
Other values (7006) 51212
44.6%
2023-08-07T16:14:00.651315image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
106547
16.5%
a 48791
 
7.6%
o 38461
 
6.0%
, 33455
 
5.2%
i 31928
 
4.9%
0 31810
 
4.9%
l 27286
 
4.2%
- 26670
 
4.1%
r 23109
 
3.6%
P 20455
 
3.2%
Other values (85) 257372
39.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 293410
45.4%
Space Separator 106547
 
16.5%
Decimal Number 92487
 
14.3%
Uppercase Letter 85417
 
13.2%
Other Punctuation 41210
 
6.4%
Dash Punctuation 26671
 
4.1%
Open Punctuation 57
 
< 0.1%
Close Punctuation 55
 
< 0.1%
Other Letter 22
 
< 0.1%
Math Symbol 6
 
< 0.1%
Other values (2) 2
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 48791
16.6%
o 38461
13.1%
i 31928
10.9%
l 27286
9.3%
r 23109
 
7.9%
e 15893
 
5.4%
u 15573
 
5.3%
n 13459
 
4.6%
d 11352
 
3.9%
ã 9618
 
3.3%
Other values (28) 57940
19.7%
Uppercase Letter
ValueCountFrequency (%)
P 20455
23.9%
S 18896
22.1%
B 10655
12.5%
R 7599
 
8.9%
C 5148
 
6.0%
A 4201
 
4.9%
V 3239
 
3.8%
M 2950
 
3.5%
J 1927
 
2.3%
E 1728
 
2.0%
Other values (20) 8619
10.1%
Decimal Number
ValueCountFrequency (%)
0 31810
34.4%
1 13755
14.9%
4 9590
 
10.4%
5 8375
 
9.1%
2 7877
 
8.5%
3 6790
 
7.3%
6 4504
 
4.9%
7 3739
 
4.0%
8 3286
 
3.6%
9 2761
 
3.0%
Other Punctuation
ValueCountFrequency (%)
, 33455
81.2%
. 7689
 
18.7%
' 34
 
0.1%
/ 30
 
0.1%
: 2
 
< 0.1%
Dash Punctuation
ValueCountFrequency (%)
- 26670
> 99.9%
1
 
< 0.1%
Open Punctuation
ValueCountFrequency (%)
( 56
98.2%
[ 1
 
1.8%
Close Punctuation
ValueCountFrequency (%)
) 54
98.2%
] 1
 
1.8%
Math Symbol
ValueCountFrequency (%)
+ 4
66.7%
| 2
33.3%
Space Separator
ValueCountFrequency (%)
106547
100.0%
Other Letter
ValueCountFrequency (%)
º 22
100.0%
Format
ValueCountFrequency (%)
 1
100.0%
Other Symbol
ValueCountFrequency (%)
° 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 378849
58.7%
Common 267035
41.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 48791
 
12.9%
o 38461
 
10.2%
i 31928
 
8.4%
l 27286
 
7.2%
r 23109
 
6.1%
P 20455
 
5.4%
S 18896
 
5.0%
e 15893
 
4.2%
u 15573
 
4.1%
n 13459
 
3.6%
Other values (59) 124998
33.0%
Common
ValueCountFrequency (%)
106547
39.9%
, 33455
 
12.5%
0 31810
 
11.9%
- 26670
 
10.0%
1 13755
 
5.2%
4 9590
 
3.6%
5 8375
 
3.1%
2 7877
 
2.9%
. 7689
 
2.9%
3 6790
 
2.5%
Other values (16) 14477
 
5.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 628769
97.4%
None 17114
 
2.6%
Punctuation 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
106547
16.9%
a 48791
 
7.8%
o 38461
 
6.1%
, 33455
 
5.3%
i 31928
 
5.1%
0 31810
 
5.1%
l 27286
 
4.3%
- 26670
 
4.2%
r 23109
 
3.7%
P 20455
 
3.3%
Other values (65) 240257
38.2%
None
ValueCountFrequency (%)
ã 9618
56.2%
í 3221
 
18.8%
é 855
 
5.0%
ó 850
 
5.0%
ç 760
 
4.4%
á 654
 
3.8%
ú 431
 
2.5%
ô 215
 
1.3%
ê 191
 
1.1%
õ 93
 
0.5%
Other values (9) 226
 
1.3%
Punctuation
ValueCountFrequency (%)
1
100.0%

bairro
Text

Distinct259
Distinct (%)3.1%
Missing0
Missing (%)0.0%
Memory size65.5 KiB
2023-08-07T16:14:01.029324image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length32
Median length28
Mean length11.660772
Min length2

Characters and Unicode

Total characters97589
Distinct characters78
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique64 ?
Unique (%)0.8%

Sample

1st rowVila Andrade
2nd rowBela Vista
3rd rowVila da Saúde
4th rowVila Gomes Cardim
5th rowJardim Vila Mariana
ValueCountFrequency (%)
vila 2329
 
15.8%
jardim 863
 
5.8%
paulista 500
 
3.4%
mariana 476
 
3.2%
pinheiros 392
 
2.7%
perdizes 360
 
2.4%
da 349
 
2.4%
itaim 285
 
1.9%
indianópolis 284
 
1.9%
santa 283
 
1.9%
Other values (277) 8635
58.5%
2023-08-07T16:14:01.582887image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 14844
15.2%
i 10899
 
11.2%
6387
 
6.5%
o 5954
 
6.1%
e 5847
 
6.0%
l 5755
 
5.9%
r 5442
 
5.6%
n 4824
 
4.9%
d 3581
 
3.7%
s 3297
 
3.4%
Other values (68) 30759
31.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 76796
78.7%
Uppercase Letter 14193
 
14.5%
Space Separator 6387
 
6.5%
Decimal Number 84
 
0.1%
Open Punctuation 54
 
0.1%
Close Punctuation 54
 
0.1%
Other Punctuation 17
 
< 0.1%
Dash Punctuation 4
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 14844
19.3%
i 10899
14.2%
o 5954
7.8%
e 5847
 
7.6%
l 5755
 
7.5%
r 5442
 
7.1%
n 4824
 
6.3%
d 3581
 
4.7%
s 3297
 
4.3%
m 2881
 
3.8%
Other values (25) 13472
17.5%
Uppercase Letter
ValueCountFrequency (%)
V 2636
18.6%
P 2052
14.5%
C 1762
12.4%
M 1271
9.0%
B 1164
8.2%
S 1057
7.4%
J 953
 
6.7%
I 821
 
5.8%
A 771
 
5.4%
L 351
 
2.5%
Other values (15) 1355
9.5%
Decimal Number
ValueCountFrequency (%)
0 16
19.0%
3 13
15.5%
1 12
14.3%
4 10
11.9%
7 8
9.5%
2 8
9.5%
6 8
9.5%
5 4
 
4.8%
8 3
 
3.6%
9 2
 
2.4%
Open Punctuation
ValueCountFrequency (%)
( 53
98.1%
[ 1
 
1.9%
Close Punctuation
ValueCountFrequency (%)
) 53
98.1%
] 1
 
1.9%
Other Punctuation
ValueCountFrequency (%)
. 15
88.2%
, 2
 
11.8%
Space Separator
ValueCountFrequency (%)
6387
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 90989
93.2%
Common 6600
 
6.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 14844
16.3%
i 10899
 
12.0%
o 5954
 
6.5%
e 5847
 
6.4%
l 5755
 
6.3%
r 5442
 
6.0%
n 4824
 
5.3%
d 3581
 
3.9%
s 3297
 
3.6%
m 2881
 
3.2%
Other values (50) 27665
30.4%
Common
ValueCountFrequency (%)
6387
96.8%
( 53
 
0.8%
) 53
 
0.8%
0 16
 
0.2%
. 15
 
0.2%
3 13
 
0.2%
1 12
 
0.2%
4 10
 
0.2%
7 8
 
0.1%
2 8
 
0.1%
Other values (8) 25
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 94815
97.2%
None 2774
 
2.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 14844
15.7%
i 10899
 
11.5%
6387
 
6.7%
o 5954
 
6.3%
e 5847
 
6.2%
l 5755
 
6.1%
r 5442
 
5.7%
n 4824
 
5.1%
d 3581
 
3.8%
s 3297
 
3.5%
Other values (56) 27985
29.5%
None
ValueCountFrequency (%)
ó 601
21.7%
ç 514
18.5%
ã 465
16.8%
í 375
13.5%
é 325
11.7%
ú 229
 
8.3%
á 97
 
3.5%
õ 82
 
3.0%
Á 56
 
2.0%
ô 27
 
1.0%
Other values (2) 3
 
0.1%
Distinct6148
Distinct (%)73.5%
Missing0
Missing (%)0.0%
Memory size65.5 KiB
2023-08-07T16:14:01.969655image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length36
Median length36
Mean length35.686701
Min length31

Characters and Unicode

Total characters298662
Distinct characters17
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4704 ?
Unique (%)56.2%

Sample

1st row23 37m 28.7148s S, 46 44m 19.55s W
2nd row23 33m 11.0052s S, 46 38m 55.3855s W
3rd row23 36m 24.8717s S, 46 38m 4.00848s W
4th row23 32m 23.086s S, 46 34m 3.2016s W
5th row23 35m 31.1615s S, 46 37m 31.8576s W
ValueCountFrequency (%)
23 8369
 
12.5%
46 8369
 
12.5%
w 8369
 
12.5%
s 8369
 
12.5%
39m 1803
 
2.7%
40m 1791
 
2.7%
36m 1677
 
2.5%
38m 1617
 
2.4%
37m 1603
 
2.4%
34m 1441
 
2.2%
Other values (12123) 23544
35.2%
2023-08-07T16:14:02.542648image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
58583
19.6%
3 33529
11.2%
4 26053
8.7%
2 22413
 
7.5%
6 19316
 
6.5%
s 16738
 
5.6%
m 16738
 
5.6%
. 16735
 
5.6%
1 12564
 
4.2%
5 11719
 
3.9%
Other values (7) 64274
21.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 164761
55.2%
Space Separator 58583
 
19.6%
Lowercase Letter 33476
 
11.2%
Other Punctuation 25104
 
8.4%
Uppercase Letter 16738
 
5.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 33529
20.4%
4 26053
15.8%
2 22413
13.6%
6 19316
11.7%
1 12564
 
7.6%
5 11719
 
7.1%
8 10823
 
6.6%
9 9956
 
6.0%
7 9745
 
5.9%
0 8643
 
5.2%
Lowercase Letter
ValueCountFrequency (%)
s 16738
50.0%
m 16738
50.0%
Other Punctuation
ValueCountFrequency (%)
. 16735
66.7%
, 8369
33.3%
Uppercase Letter
ValueCountFrequency (%)
S 8369
50.0%
W 8369
50.0%
Space Separator
ValueCountFrequency (%)
58583
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 248448
83.2%
Latin 50214
 
16.8%

Most frequent character per script

Common
ValueCountFrequency (%)
58583
23.6%
3 33529
13.5%
4 26053
10.5%
2 22413
 
9.0%
6 19316
 
7.8%
. 16735
 
6.7%
1 12564
 
5.1%
5 11719
 
4.7%
8 10823
 
4.4%
9 9956
 
4.0%
Other values (3) 26757
10.8%
Latin
ValueCountFrequency (%)
s 16738
33.3%
m 16738
33.3%
S 8369
16.7%
W 8369
16.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 298662
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
58583
19.6%
3 33529
11.2%
4 26053
8.7%
2 22413
 
7.5%
6 19316
 
6.5%
s 16738
 
5.6%
m 16738
 
5.6%
. 16735
 
5.6%
1 12564
 
4.2%
5 11719
 
3.9%
Other values (7) 64274
21.5%

Interactions

2023-08-07T16:13:54.624407image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-07T16:13:46.742992image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-07T16:13:47.901149image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-07T16:13:49.039956image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-07T16:13:50.189732image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-07T16:13:51.287421image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-07T16:13:52.506564image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-07T16:13:53.545700image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-07T16:13:54.772167image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-07T16:13:46.897736image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-07T16:13:48.033091image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-07T16:13:49.194759image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-07T16:13:50.333403image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-07T16:13:51.458481image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-07T16:13:52.635995image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-07T16:13:53.681104image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-07T16:13:54.899533image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-07T16:13:47.053846image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-07T16:13:48.149332image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-07T16:13:49.345239image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-07T16:13:50.458245image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-07T16:13:51.688286image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-07T16:13:52.756608image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-07T16:13:53.808117image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-07T16:13:55.035238image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-07T16:13:47.216648image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-07T16:13:48.286172image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-07T16:13:49.497741image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-07T16:13:50.598904image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-07T16:13:51.828198image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-07T16:13:52.890030image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-07T16:13:53.944685image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-07T16:13:55.176564image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-07T16:13:47.358064image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-07T16:13:48.428350image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-07T16:13:49.642918image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-07T16:13:50.746428image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-07T16:13:51.977101image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-07T16:13:53.025812image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-07T16:13:54.087143image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-07T16:13:55.316851image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-07T16:13:47.500986image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-07T16:13:48.615814image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-07T16:13:49.783183image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-07T16:13:50.889469image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-07T16:13:52.108562image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-07T16:13:53.160986image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-07T16:13:54.221581image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-07T16:13:55.448858image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-07T16:13:47.630339image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-07T16:13:48.781546image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-07T16:13:49.912686image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-07T16:13:51.020891image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-07T16:13:52.240357image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-07T16:13:53.284341image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-07T16:13:54.351440image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-07T16:13:55.585745image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-07T16:13:47.764238image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-07T16:13:48.908541image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-07T16:13:50.051270image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-07T16:13:51.155251image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-07T16:13:52.376482image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-07T16:13:53.415932image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-07T16:13:54.482211image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-08-07T16:14:02.687900image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
roomsgaragesuseful_arealatitudelongitudevaluetime_on_marketUnnamed: 0interior_qualitysold
rooms1.0000.6640.765-0.033-0.1140.6690.082-0.0070.0000.109
garages0.6641.0000.712-0.124-0.1830.6760.093-0.0560.0000.124
useful_area0.7650.7121.0000.004-0.1530.9170.119-0.0130.0000.154
latitude-0.033-0.1240.0041.0000.0540.0360.0200.0340.0000.068
longitude-0.114-0.183-0.1530.0541.000-0.174-0.0340.0530.0000.100
value0.6690.6760.9170.036-0.1741.0000.224-0.0270.0200.268
time_on_market0.0820.0930.1190.020-0.0340.2241.000-0.0420.0490.371
Unnamed: 0-0.007-0.056-0.0130.0340.053-0.027-0.0421.0000.0000.039
interior_quality0.0000.0000.0000.0000.0000.0200.0490.0001.0000.052
sold0.1090.1240.1540.0680.1000.2680.3710.0390.0521.000

Missing values

2023-08-07T16:13:55.778442image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-08-07T16:13:56.041503image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

roomsgaragesuseful_arealatitudelongitudevalueinterior_qualitytime_on_marketsoldUnnamed: 0endereçobairropoint_location
03.02.077-23.624629-46.738757686126275.83827300R. José de Oliveira Coelho, 200 - Vila Andrade, São Paulo - SP, 05727-240, BrazilVila Andrade23 37m 28.7148s S, 46 44m 19.55s W
11.01.035-23.553170-46.6486876751523215.49061801R. Araquã, 70 - Bela Vista, São Paulo - SP, 01306-020, BrazilBela Vista23 33m 11.0052s S, 46 38m 55.3855s W
23.02.097-23.607094-46.634414871258292.04820002Rua Correia de Lemos, 327 - Vila da Saúde, São Paulo - SP, 04140-000, BrazilVila da Saúde23 36m 24.8717s S, 46 38m 4.00848s W
33.03.0102-23.539732-46.567559648740332.00000013R. Itapura, 129 - Vila Gomes Cardim, São Paulo - SP, 03310-000, BrazilVila Gomes Cardim23 32m 23.086s S, 46 34m 3.2016s W
42.01.078-23.591993-46.6254819686432319.57086604R. Garapeba, 255 - Jardim Vila Mariana, São Paulo - SP, 04116-210, BrazilJardim Vila Mariana23 35m 31.1615s S, 46 37m 31.8576s W
53.03.0210-23.616330-46.6825412175420293.35525505Condomínio Edificio Harmony Plan - R. Flórida, 169 - Vila Cordeiro, São Paulo - SP, 04565-000, BrazilVila Cordeiro23 36m 58.7837s S, 46 40m 57.1447s W
62.01.070-23.546449-46.650388592282220.00000016R. Dr. Vila Nova, 300 - Vila Buarque, São Paulo - SP, 01222-020, BrazilVila Buarque23 32m 47.1815s S, 46 39m 1.55772s W
73.02.094-23.611557-46.63519997277211.15173007Praça da Árvore, 300 - Bosque da Saúde, São Paulo - SP, 04142-000, BrazilBosque da Saúde23 36m 41.94s S, 46 38m 6.93348s W
84.04.0220-23.625767-46.731048128410035.00000018R. Frederico Guarinon, 125 - Jardim Ampliacao, São Paulo - SP, 05713-460, BrazilJardim Ampliacao23 37m 32.7594s S, 46 43m 51.7721s W
92.01.0110-23.533349-46.70430584464339.00000019Rua Tonelero, 593 - Vila Ipojuca, São Paulo - SP, 05056-000, BrazilVila Ipojuca23 32m 0.02112s S, 46 42m 15.5207s W
roomsgaragesuseful_arealatitudelongitudevalueinterior_qualitytime_on_marketsoldUnnamed: 0endereçobairropoint_location
83593.02.083-23.590720-46.718816754080213.20173406286R. Dom Armando Lombardi, 643 - Vila Progredior, São Paulo - SP, 05616-011, BrazilVila Progredior23 35m 26.7781s S, 46 43m 7.72068s W
83603.02.0126-23.636496-46.645191665930335.00000011309R. Freire Farto, 58 - Jabaquara, São Paulo - SP, 04343-120, BrazilJabaquara23 38m 11.7881s S, 46 38m 43.0318s W
83611.01.050-23.628118-46.6350124455193190.88939806287Unidade - R. Maj. Freire, 594 - Vila Monte Alegre, São Paulo - SP, 04303, BrazilVila Monte Alegre23 37m 41.2709s S, 46 38m 6.30672s W
83621.01.043-23.540164-46.65054550779928.00000014983Rua Dona Veridiana, 64a - Higienópolis, São Paulo - SP, 01238-010, BrazilHigienópolis23 32m 24.9392s S, 46 39m 1.96092s W
83634.02.0160-23.628829-46.63571814256602288.39086506288R. dos Democratas, 691 - Vila Monte Alegre, São Paulo - SP, 04305-000, BrazilVila Monte Alegre23 37m 43.1263s S, 46 38m 8.5668s W
83642.01.078-23.584908-46.6761799983821328.75128906289R. Joaquim Floriano, 640 - Itaim Bibi, São Paulo - SP, 45340-002, BrazilItaim Bibi23 35m 5.66916s S, 46 40m 34.3265s W
83652.01.066-23.620348-46.6405905344782331.33699306290Condomínio Edifício Uvaias - R. das Uvaias, 145 - Vila da Saúde, São Paulo - SP, 04055-110, BrazilVila da Saúde23 37m 13.2514s S, 46 38m 26.1233s W
83664.04.0263-23.499382-46.6418732214300166.53260505411R. Benta Pereira, 315 - Santa Teresinha, São Paulo - SP, 02451-000, BrazilSanta Teresinha23 29m 58.1248s S, 46 38m 31.0474s W
83671.01.043-23.624537-46.6696734195392276.04691606291Condomínio Edifício Mila - R. Vicente Leporace, 1320 - Campo Belo, São Paulo - SP, 04619-033, BrazilCampo Belo23 37m 28.3537s S, 46 40m 10.7605s W
83681.01.040-23.619506-46.6405852522951100.00000014467Condomínio Edifício Claudia - R. das Uvaias, 52 - Mirandópolis, São Paulo - SP, 04055-110, BrazilMirandópolis23 37m 10.2198s S, 46 38m 26.1028s W

Duplicate rows

Most frequently occurring

roomsgaragesuseful_arealatitudelongitudevalueinterior_qualitytime_on_marketsoldUnnamed: 0endereçobairropoint_location# duplicates
02.01.057-23.574803-46.614062297708112.01771R. Dom Mateus, 44 - 10º andar - Vila Monumento, São Paulo - SP, 01548-030, BrazilVila Monumento23 34m 29.3628s S, 46 36m 50.598s W2